Developing and Validating Clinical Prediction Models in Hepatology—an Overview for Clinicians

Summary

Prediction models are everywhere in clinical medicine. We use them to assign a diagnosis or a prognosis, and there is a continuous effort to develop better prediction models. It is important to understand the fundamentals of prediction modeling, and here we describe nine steps to develop and validate a clinical prediction model with the intention of implementing it in clinical practice: Determine if there is a need for a new prediction model; define the purpose and intended use for the model; assess the quality and quantity of the data you wish to develop the model on; develop the model using sound statistical methods; generate risk predictions on the probability scale (0-100%); evaluate the performance of the model in terms of discrimination, calibration, and clinical utility; validate the model using bootstrapping to correct for the apparent optimism in performance; validate the model on external datasets to assess the generalizability and transportability of the model; and finally publish the model so that it can be implemented or validated by others.